Mining Noisy Transaction Data with Neura...

Extracting relevant information from unstructured transaction data presents a challenge for those who may want to use such data for making business decisions such as underwriting loans or for monitoring credit worthiness. Most of our transaction data is in the form of transaction text describing the transaction often using abbreviations or unknown proper nouns. A common approach for text documents is to encode the words or documents into vectors using a neural net layer or multiple layers. These features may then be used in a classification algorithm or other models for predicting an outcome. To this end, we encoded transaction data of small 'sentences', often of only a few words, using skip-gram word2vec models along with RBM and Deep Belief Nets utilizing other features such as credit or debit value of transaction and institution information. The goal of this discussion is to describe the performance of the model and also considerations for training a nn in a large-data distributed framework like Spark. Tools used are Deeplearning4j, Spark, Scala.